model_Qwen2_Grupo6_FINAL

Grupo 6:

• Aruquipa Chambi Marcelo German
• Ascencio Andrade Roberto Carlos
• Fuentes Ramos Percy Pedro
• Miranda Nina Juan Marcos
• Romero Flores Karina Raquel
• Torrez Marca Gerson Jesus

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base_model: Qwen/Qwen2.5-0.5B-Instruct library_name: peft model_name: qwen-decomposition-lora tags:

• base_model:adapter:Qwen/Qwen2.5-0.5B-Instruct
• lora
• sft
• transformers
• trl licence: license pipeline_tag: text-generation

---

Model Card for qwen-decomposition-lora

This model is a fine-tuned version of Qwen/Qwen2.5-0.5B-Instruct. It has been trained using TRL.

Quick start

from transformers import pipeline

question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="None", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])

Training procedure

This model was trained with SFT.

Framework versions

• PEFT 0.17.1
• TRL: 0.24.0
• Transformers: 4.57.6
• Pytorch: 2.8.0
• Datasets: 4.5.0
• Tokenizers: 0.22.2

Citations

Cite TRL as:

@misc{vonwerra2022trl,
	title        = {{TRL: Transformer Reinforcement Learning}},
	author       = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
	year         = 2020,
	journal      = {GitHub repository},
	publisher    = {GitHub},
	howpublished = {\url{https://github.com/huggingface/trl}}
}